通过通过动态轨迹生成模仿-放松强化学习来进行稀疏的羽毛球击球
Yanyan Yuan1,2,3,4, Yucheng Tao1,2,3,4, Shaowen Cheng1,2,3,4
1Center for X-Mechanics, Zhejiang University, Hangzhou, China.
Frontiers in neurorobotics
|September 18, 2025
概括
这项研究引入了机器人羽毛球击球的新框架,提高了训练效率和准确性. DTG-IRRL (动态轨迹生成模仿-放松强化学习) 框架增强了机器人的动态运动控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 机器人球拍运动,比如羽毛球,由于复杂的飞动力学和强化学习的稀疏奖励问题而具有挑战性.
- 在快节奏运动中的机器人中实现精确的动态运动控制是一个重大障碍.
研究的目的:
- 开发一个新的学习框架,DTG-IRRL,以提高机器人执行羽毛球击球的性能.
- 为解决机器人羽毛球的强化学习中非线性动态和稀少奖励的挑战.
主要方法:
- 模仿放松强化学习与动态轨迹生成的整合.
- 在特定的参数空间内分析奖励函数的趋同,以了解学习困难.
- 在硬件上实现零射击转移,用于现实世界的测试.
主要成果:
- DTG-IRRL框架显著提高了培训效率和更快的融合.
- 与以前的方法相比,实现了两倍的着陆精度.
- 硬件实现导致了90%的击中率和70%的着陆精度,使得持续的人机器人集会成为可能.
结论:
- 拟议的DTG-IRRL框架有效地缓解了机器人羽毛球中奖励稀少造成的融合问题.
- 该研究强调了框架在不同机器人平台上的通用性,比如UR5机器人.
- 机器人手臂的高动态性能对于球拍运动应用中的成功至关重要.
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